Risk environment modeling for predicting decisions

• US 20060247956A1
• Filed: 06/24/2004
• Published: 11/02/2006
• Est. Priority Date: 06/24/2003
• Status: Abandoned Application

• Current Assignee: R-Squared Analytics LLC
• Original Assignee: R-Squared Analytics LLC
• Inventors: Bly, Bridget, Rosen, Daniel Jonathan
• Application Number:
  US10/561,750
• Publication Number:
  US 20060247956A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A modeling method for predicting a decision is disclosed. A risk environment is simulated for one or more control groups. One or more experimental groups are exposed to an intervention, and the risk environment is simulated for the experimental groups.

Claims

1. A modeling method for predicting a decision, comprising:

simulating a risk environment for one or more control groups,

exposing one or more experimental groups to an intervention, and

simulating the risk environment for the one or more experimental groups.

2. The method of claim 1 further comprising:

constructing a first model of a relationship between the intervention and a perception,

constructing a second model of a relationship between the perception and a decision,

calibrating the first and second models using a set of real world data, and

predicting the decision using the first and second models.

3. The method of claim 1 wherein simulating a risk environment further comprises:

questioning a subject on one or more relevant factors,

offering to the subject a plurality of choices for the decision,

offering to the subject an incentive, and

recording a selected choice made by the subject.

4. The method of claim 3 wherein the step of simulating a risk environment for one or more control groups further comprises:

determining whether a set of experimental data for a first control group sufficiently matches a set of real world data, and if not,

(a) adjusting one or more design parameters for a second control group, and

(b) simulating the risk environment for the second control group.

5. The method of claim 4 wherein the one or more design parameters comprise the incentive.

6. The method of claim 4 wherein the one or more design parameters comprise at least a portion of the plurality of choices.

7. The method of claim 4, the plurality of choices having a set of orthogonal characteristics, wherein the step of adjusting further comprises:

conjointly analyzing the set of experimental data with the set of orthogonal characteristics.

8. The method of claim 3 wherein the plurality of choices comprises at least one product choice and a non-selection choice.

9. The method of claim 3 further comprising providing an item of value to a subject, and wherein the incentive comprises:

a risk of losing at least a portion of the item of value, and

a reward of a further item of value.

10. The method of claim 3 wherein the item of value comprises money.

11. The method of claim 3 further comprising providing a period of time to a subject, wherein the incentive comprises:

a benefit associated with the selected choice,

a cost associated with the selected choice, the cost comprising at least a portion of the period of time, and

an opportunity cost comprising a lost benefit associated with one or more non-selected choices.

12. The method of claim 3 further comprising:

questioning the subject on one or more diversionary factors,

falsely describing a profile associated with the diversionary factors,

falsely describing a contingency of the incentive upon a match between the selected choice and an objective choice associated with the profile.

13. The method of claim 1 further comprising calibrating the risk environment.

14. The method of claim 1 wherein the decision relates to a financial transaction.

15. The method of claim 1 wherein the decision relates to a consumer purchase.

27. The method of claim 1 wherein the decision relates to voting.

16. A method for modeling decision making behavior, comprising:

providing a simulated risk environment to one or more control groups,

calibrating the simulated risk environment against a set of real world data,

providing an intervention to one or more experimental groups,

providing the simulated risk environment to the one or more experimental groups,

modeling a relationship between the intervention and a perception,

modeling a relationship between the perception and a decision,

calibrating one or more models against the set of real world data, and

obtaining one or more predictions using the one or more models.

17. The method of claim 16 wherein providing the simulated risk environment further comprises:

questioning a subject on one or more relevant factors,

offering to the subject a plurality of choices for the decision, the plurality of choices comprising at least one product choice and a non-selection choice,

offering to the subject an incentive, and

recording a selected choice made by the subject.

18. The method of claim 16 wherein the step of calibrating the simulated risk environment further comprises:

determining whether a set of experimental data for a first control group adequately matches at least a portion of the set of real world data, and if not,

(a) adjusting one or more design parameters of the simulated risk environment, and

(b) providing the simulated risk environment to a second control group.

19. The method of claim 18 further comprising offering to the subject a plurality of choices for the decision, the plurality of choices having a set of orthogonal characteristics, wherein the step of adjusting further comprises conjointly analyzing the set of experimental data with the set of orthogonal characteristics.

28. The method of claim 16 wherein the decision relates to voting.

20. A risk environment system for modeling a decision of a participant, comprising:

an item of value,

at least one intervention,

a plurality of questions comprising at least one non-diversionary question and at least one diversionary question,

a plurality of choices comprising at least one product choice and a non-selection choice, and

an incentive comprising a risk associated with a selected choice and a reward associated with the selected choice.

21. A simulated risk environment system for modeling a behavior of one or more subjects, comprising:

at least one intervention,

a plurality of questions,

a plurality of choices for spending a period of time, and

an incentive to a subject comprising;

(a) a benefit associated with a selected choice,

(b) a cost associated with the selected choice, the cost comprising at least a portion of the period of time, and

(c) an opportunity cost comprising a lost benefit associated with one or more non-selected choices.

22. A computer-readable storage medium containing a set of instructions for simulating a risk environment for one or more subjects, the instructions comprising:

a code segment for presenting to a subject questions on one or more relevant factors,

a code segment for offering to the subject a plurality of choices for a decision,

a code segment for offering to the subject an incentive, and a code segment for recording a selected choice made by the subject.

23. The computer-readable storage medium of claim 22, the instructions further comprising:

a code segment for questioning the subject on one or more non-relevant factors,

a code segment for falsely describing a profile associated with the non-relevant factors,

a code segment for falsely describing a contingency of the incentive upon a match between the decision and an objective decision associated with the profile.

24. A computer-implemented system for modeling at least one effect of an intervention, comprising a computer, and one or more software applications that comprise steps for:

presenting to a subject questions on one or more relevant factors,

offering to the subject a plurality of choices for a decision,

offering to the subject an incentive,

recording a selected choice made by the subject,

constructing a first model of a relationship between the intervention and a perception,

constructing a second model of a relationship between the perception and a behavior,

calibrating the first and second models using a set of real world data, and

obtaining at least one prediction using the first and second models.

25. The system of claim 24 wherein the one or more software applications further comprise steps for exposing the subject to the intervention.

26. The system of claim 24 wherein the decision relates to voting.